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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Mohhamied Husaein Fallaah T. Shanthi S. Balakrishna Reddy Rajavadhani K K. Sandhya Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Sona College of Technology Salem India Department of Computer Science and Engineering (DS) CVR College of Engineering Hyderabad India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram India Department of Computer Science and Engineering (AI&ML) Mohan Babu University Tirupati India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In recent years, the rapid development of the Internet of Things (IoT) has made fault tolerance is important for maintaining service quality and system availability. Fault tolerance Quality of Service (QoS) provisioning ensures IoT systems operate continuously, even in presence of failures or malfunctions. Traditional approaches for QoS provisioning had face several challenges which include data security risks, inconsistent updates. Therefore, this research proposes One Class Support Vector Machine (OCSVM) for fault tolerant QoS provisioning in IoT systems. Initially, data is collected from Message Queuing Telemetry Transport-IoT-Intrusion Detection System (MQTT-IoT-IDS 2020) and preprocessed by using Expectation Maximization (EM) which reduces noise in data and handles missing data. Finally, provisioning and fault detection is done by using proposed OCSVM which detected faults and communication errors. The proposed OCSVM achieved better results in Execution time (220.61), Execution Cost (793.02) and Energy Consumption (22J) when compared with existing Fault-Tolerant and Data-Oriented Scientific Workflow Management and Scheduling System (FD-SWMS).

关键词： Support vector machines Fault tolerance Costs Fault detection Computational modeling Fault tolerant systems Noise Quality of service Feature extraction Internet of Things

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The Distributed Intelligent Collaboration to UAV-Assisted VEC: Joint Position Optimization and Task Scheduling

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IEEE Internet of Things Journal 2025年第12期12卷 21473-21487页

作者： Yi, Meng Lee, Vincent C.S. Yang, Peng Li, Peisong Zhang, Yifan Wei, Wei Gao, Honghao Southeast University School of Computer Science and Engineering Nanjing211189 China Monash University Faculty of IT Department of DS and AI ClaytonVIC3800 Australia Southeast University School of Computer Science and Engineering the Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing211189 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China Southeast University-Monash University Joint Graduate School Suzhou215123 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Shanghai University School of Computer Engineering and Science Shanghai200444 China

Deploying Unmanned Aerial Vehicles (UAVs) as aerial base stations enhances the coverage and performance of communication networks in Vehicular Edge Computing (VEC) scenarios. However, due to the limited communication range and energy capacity of UAVs, they cannot continuously cover entire areas or sustain long flights. Therefore, achieving full communication coverage of a target area with a minimal number of UAVs and efficient task offloading remains a significant challenge. To address this problem, the UAV-assisted Two-stage Intelligent Collaboration (UTIC) method is proposed in this paper to tackle the joint position optimization and task scheduling issue. Firstly, a UAV-assisted Two-stage Task Scheduling (UTTS) system model is designed to optimize the allocation process. Secondly, an Enhanced Particle Swarm Optimization (E-PSO) algorithm is designed to determine the optimal positions of UAVs, ensuring complete coverage of all mobile vehicles (MVs) with the minimum number of UAVs. Thirdly, Deep Deterministic Policy Gradient (DDPG) method is employed to find the optimal scheduling decisions for MVs, considering energy consumption, delay, and task priorities. Simulation results demonstrate that the proposed UTIC method can achieve nearly 20% reduction in UAV deployment and outperform three other classical reinforcement learning (RL) algorithms in terms of reducing system cost. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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HOW ROBUST IS UNSUPERVISED REPRESENTATION LEARNING TO DISTRIBUTION SHIFT? 11

HOW ROBUST IS UNSUPERVISED REPRESENTATION LEARNING TO DISTRI...

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11th International Conference on Learning Representations, ICLR 2023

作者： Shi, Yuge Daunhawer, Imant Vogt, Julia E. Torr, Philip H.S. Sanyal, Amartya Department of Engineering Science University of Oxford United Kingdom Department of Computer Science ETH Zurich Switzerland Department of Computer Science ETH AI Center ETH Zurich Switzerland

The robustness of machine learning algorithms to distributions shift is primarily discussed in the context of supervised learning (SL). As such, there is a lack of insight on the robustness of the representations learned from unsupervised methods, such as self-supervised learning (SSL) and auto-encoder based algorithms (AE), to distribution shift. We posit that the input-driven objectives of unsupervised algorithms lead to representations that are more robust to distribution shift than the target-driven objective of SL. We verify this by extensively evaluating the performance of SSL and AE on both synthetic and realistic distribution shift datasets. Following observations that the linear layer used for classification itself can be susceptible to spurious correlations, we evaluate the representations using a linear head trained on a small amount of out-of-distribution (OOD) data, to isolate the robustness of the learned representations from that of the linear head. We also develop "controllable" versions of existing realistic domain generalisation datasets with adjustable degrees of distribution shifts. This allows us to study the robustness of different learning algorithms under versatile yet realistic distribution shift conditions. Our experiments show that representations learned from unsupervised learning algorithms generalise better than SL under a wide variety of extreme as well as realistic distribution shifts. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Supervised learning

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A Comprehensive Analysis of Word Sense Disambiguation in a Regional Language

A Comprehensive Analysis of Word Sense Disambiguation in a R...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Aditi Barai Manami Das Priyanka Bhowmick Udita Dey Prianka Dey Sagarika Chowdhury Department of Computer Science and Business Systems Narula Institute of Technology Kolkata India Department of Computer Science and Engineering (AI ML) Narula Institute of Technology Kolkata India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Word Sense Disambiguation(WSD) is a Natural Language Processing(NLP) technique that tries to disambiguate ambiguous words by finding the right sense of a word in a particular context. It is a classification task that assists in mapping a word to one or more potential senses based on its sentence. Understanding the intended sense in a particular context is crucial for proper communication and accurate language processing. But WSD is especially crucial in Bengali for a number of reasons: Similar to other Indic languages, Bengali possesses a rich and complex system of word formation. A root word can be modified according to aspects like tense, gender, number, and case. An attempt is made in this working paper where authors will be working on WSD but on the Bengali language, where words have different meanings. The Bengali language has plenty of words with multiple meanings. Even though WSD has been propagated the world over, the language Bengali has a lot of challenges since there are very few resources available and research due to its complex syllable structure.

关键词： Surveys Sensitivity Computational modeling Evaluation models Natural languages Computational efficiency Complexity theory Object recognition Optimization Overfitting

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Virtual-event-based posterior sampling and inference for Neyman-Scott processes

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 18784-18850页

作者： Chengkuan Hong Christian R. Shelton Jun Zhu Department of Computer Science and Technology BNRist Center Tsinghua Institute for AI Tsinghua-Bosch Joint Center for ML Tsinghua University Beijing China Department of Computer Science and Engineering University of California Riverside CA

Neyman-Scott processes (NSPs) are a class of Cox processes constructed by stacking layers of Poisson processes into a deep structure. While a lot of research has been conducted regarding the posterior sampling and inference for NSPs, most of the existing methods only work for shallow NSPs (i.e., NSPs with one layer of latent Poisson processes). In this paper, we present virtual-event-based posterior sampling and inference algorithms for NSPs. The algorithms work for both deep NSPs and shallow NSPs. Moreover, we show that deep NSPs can be viewed as branching processes or a limiting case of probabilistic graphical models. We conduct a theoretical analysis of the convergence of our algorithms and provide the condition for the convergence to hold. In doing so, we also prove the convergence of virtual-event-based sampling inference algorithms for other point process models with missing information (Markov jump processes, piecewise-constant intensity models, and Hawkes processes). Like NSPs, the latent variables of these models with missing information are also point processes. Our experimental results demonstrate that the prediction based on our sampling and inference algorithms for NSPs can achieve good prediction performance compared with state-of-the-art methods.

关键词： Markov chain Monte Carlo variational inference point processes hierarchical model Neyman-Scott processes

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Classification of Various Diseases in Tomato Crops using Neural Network Approaches

Classification of Various Diseases in Tomato Crops using Neu...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Kodem Sravan U Sadhana Sallauddin Mohmmad Bobby K Simon K Sridhar School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of CSE(AI&ML) CVR College of Engineering Hyderabad Telangana India Department of Computer Science and Engineering Hyderabad Institute of Technology and Management Hyderabad Telangana India Vaageswari College of Engineering Karimnager Telangana India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Tomatoes are an important crop worldwide, playing a substantial role in the agriculture sector. However, they are sensitive to various diseases that can affect yield, quality of crops, and former economic stability. This research focused on developing a system for classifying tomato diseases based on leaf images using machine learning and image processing techniques. Our research utilized convolutional neural networks (CNNs) to predict and classify tomato diseases, employing a dataset from Kaggle containing five disease classes and one healthy leaf class. Our optimized models were tested, showing inconsistent accuracy in detecting and classifying five different tomato diseases. Apart from that, MobileNet and VGG-19 models showed prominent performance and achieved an accuracy of 93.87% compared to other models such as VGG16 and a Customized CNN.

关键词： Technological innovation Accuracy Biological system modeling Image processing Neural networks Crops Stability analysis Convolutional neural networks Protection Diseases

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CM-Off-Meme: Code-Mixed Hindi-English Offensive Meme Detection with Multi-Task Learning by Leveraging Contextual Knowledge 30

CM-Off-Meme: Code-Mixed Hindi-English Offensive Meme Detecti...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Kumari, Gitanjali Bandyopadhyay, Dibyanayan Ekbal, Asif Vinutha, B.N. Department of Computer Science and Engineering Indian Institute of Technology Patna Bihta India Wipro AI Labs Karnataka Bangalore India

ISBN: (纸本)9782493814104

Detecting offensive content in internet memes is challenging as it needs additional contextual knowledge. While previous works have only focused on detecting offensive memes, classifying them further into implicit and explicit categories depending on their severity is still a challenging and underexplored area. In this work, we present an end-to-end multitask model for addressing this challenge by empirically investigating two correlated tasks simultaneously: (i) offensive meme detection and (ii) explicit-implicit offensive meme detection by leveraging the two self-supervised pre-trained models. The first pre-trained model, referred to as the "knowledge encoder," incorporates contextual knowledge of the meme. On the other hand, the second model, referred to as the "fine-grained information encoder", is trained to understand the obscure psycho-linguistic information of the meme. Our proposed model utilizes contrastive learning to integrate these two pre-trained models, resulting in a more comprehensive understanding of the meme and its potential for offensiveness. To support our approach, we create a large-scale dataset, CM-Off-Meme, as there is no publicly available such dataset for the code-mixed Hindi-English (Hinglish) domain. Empirical evaluation, including both qualitative and quantitative analysis, on the CM-Off-Meme dataset demonstrates the effectiveness of the proposed model in terms of cross-domain generalization. The sample dataset and codes are available at this link: https://***/~ai-nlp-ml/*** as well as at our GitHub repository: https://***/Gitanjali1801/CM_***. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Large datasets

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A Reinforcement Learning Approach for Intelligent Conversational Chatbot for Enhancing Mental Health Therapy 2

A Reinforcement Learning Approach for Intelligent Conversati...

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2nd International Conference on Machine Learning and Data engineering, ICmlDE 2023

作者： Abubakar, Abdulqahar Mukhtar Gupta, Deepa Parida, Shantipriya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Silo AI Helsinki Finland

Mental health therapy plays a crucial role in addressing the growing challenges of mental health disorders. However, barriers such as limited accessibility and social stigma often hinder individuals from seeking timely and effective support. Advancement in the field of Artificial Intelligence (ai) and Natural Language Processing (NLP) has enabled the development of Chatbots which can simulate counseling conversations and provide support to those who seek help. However, existing chatbots often lack contextual understanding, limit user responses to multiple-choice questions, and face challenges in generating human-like responses. In this work, we present an Intelligent Conversational Agent using Reinforcement Learning with Human Feedback (RLHF) for mental health therapy support. The proposed system employs reinforcement learning to optimize its responses and adapts its conversational strategies to better understand user's responses, analyze the emotions expressed, and provide personalized therapeutic interventions to enhance Mental Health Therapy. © 2024 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

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End-to-end differentiability and tensor processing unit computing to accelerate materials’ inverse design

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npj Computational Materials 2023年第1期9卷 1072-1083页

作者： Han Liu Yuhan Liu Kevin Li Zhangji Zhao Samuel S.Schoenholz Ekin D.Cubuk Puneet Gupta Mathieu Bauchy SOlids inFormaTics AI-Laboratory(SOFT-AI-Lab) College of Polymer Science and EngineeringSichuan UniversityChengdu 610065China AIMSOLID Research Wuhan 430223China Physics of AmoRphous and Inorganic Solids Laboratory(PARISlab) Department of Civil and Environmental EngineeringUniversity of CaliforniaLos AngelesCA 90095USA Department of Computer Science University of CaliforniaLos AngelesCA 90095USA Google Research Brain TeamMountain ViewCAUSA Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA 90095USA

Numerical simulations have revolutionized material ***,although simulations excel at mapping an input material to its output property,their direct application to inverse design has traditionally been limited by their high computing cost and lack of ***,taking the example of the inverse design of a porous matrix featuring targeted sorption isotherm,we introduce a computational inverse design framework that addresses these challenges,by programming differentiable simulation on TensorFlow platform that leverages automated end-to-end *** to its differentiability,the simulation is used to directly train a deep generative model,which outputs an optimal porous matrix based on an arbitrary input sorption isotherm ***,this inverse design pipeline leverages the power of tensor processing units(TPU)—an emerging family of dedicated chips,which,although they are specialized in deep learning,are flexible enough for intensive scientific *** approach holds promise to accelerate inverse materials design.

关键词： enough inverse isotherm

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Kim, Gyeong-Hyeon Ko, Nayoung Kim, Eunwoo School of Computer Science and Engineering Chung-Ang University Korea Republic of Chung-Ang University Department of Ai Korea Republic of

ISBN: (纸本)9798350364637

Class-incremental semantic segmentation faces challenges in retaining the knowledge of old classes while learning new ones. Catastrophic forgetting and background shift are the primary hurdles causing performance degradation. Moreover, class confusion arises when new classes share categorical similarities with existing ones. Due to class confusion, the class-incremental semantic segmentation model overwrites the knowledge of old classes with similar new classes. In this paper, we introduce Similarity-aware Class Discrimination (SCD) to address the class confusion problem. SCD increases the distance between classes that share categorical similarities (i.e., visually similar but different classes) in the embedding space, preventing new knowledge from overwriting old knowledge. This approach encourages the model to discriminate between similar categorical classes, mitigating class confusion in class-incremental semantic segmentation. Our method surpasses existing CNN-based and transformer-based class-incremental semantic segmentation methods in PASCAL VOC 2012. © 2024 IEEE.

关键词： Semantic Segmentation

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